Machine Learning Engineer, Ranking & Retrieval

About the role

A startup building AI-native applications for everyday communication, coordination and collaboration is hiring a Machine Learning Engineer focused on ranking and retrieval. The engineer builds and improves ML systems, from model development through production deployment, and works on classification, retrieval, ranking, personalization and evaluation.

Key responsibilities

• Build and improve ML models for classification, ranking, retrieval and personalization
• Develop efficient classifiers and optimize accuracy, precision, latency and cost
• Improve search relevance and context retrieval using embeddings, hybrid search and reranking
• Build personalization models that learn from user behavior while respecting privacy
• Develop evaluation pipelines, labeled datasets and metrics to measure model quality
• Fine-tune and adapt existing models where enough data is available
• Maintain ML training, inference and deployment pipelines
• Debug production issues, monitor model performance and ship improvements based on real-world feedback

Required profile

• Strong foundations in machine learning and modern neural architectures
• Hands-on experience building and deploying ML models in production
• Experience in classification, ranking, retrieval, recommendation systems or personalization
• Understanding of model evaluation, experimentation and performance metrics
• Comfortable writing production-quality code and working with real-world datasets
• Able to balance model quality, latency, cost and reliability; strong ownership and problem-solving skills

Required skills

• Python
• PyTorch / JAX
• Embeddings, vector search and ranking models
• Production ML systems and inference pipelines

The interview process involves 3 to 4 virtual and/or onsite interviews, evaluated by the technical team, with a prompt decision.